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检索条件"主题词=filtering algorithms"

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Three-dimensional Reconstruction of Wire-structured Optical Welds and Point Cloud filtering Methods

Three-dimensional Reconstruction of Wire-structured Optical ...

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Yili Shen Chuixi Zeng School of Mechanical and Resource Engineering Wuzhou University Wuzhou China Technical Department Guangxi Aiibbot Intelligent Technology Co. Nanning China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Three-dimensional reconstruction of weld surfaces is essential for evaluating weld quality. However, the fixed positions of the camera and laser light source prevent direct reconstruction within the camera's coordinate system using line structured light scanning. To address this, we mounted the camera and laser light source on a six-axis robot and calibrated the transformation matrix between the base and camera coordinate systems. This setup allows us to generate a point cloud representing the weld surface within the base coordinate system. Considering the specific characteristics of 3D reconstruction and the topographic features of weld surfaces, we developed a reduced-dimensional point cloud filtering method. Comparative analysis with traditional filtering algorithms shows that this method effectively eliminates isolated and cluster noise, ensuring accurate topographic reconstruction of weld surfaces.

关键词： Point cloud compression Surface reconstruction Three-dimensional displays filtering Welding Robot vision systems Noise filtering algorithms Cameras Surface topography

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GMM-Based Distributed Extended Set-Membership filtering for Target Tracking Under Cyberattacks

GMM-Based Distributed Extended Set-Membership Filtering for ...

引用

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Yuan Fu Hongbo Zhu School of Electrical and Information Engineering Anhui University of Science and Technology Huainan China

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

For target tracking in wireless sensor networks under cyberattacks, this paper constructs a Gaussian-mixture-model-based (GMM-based) distributed robust extended set-membership filtering algorithm, aiming to improve the consistency and accuracy of mobile target tracking under malicious cyberattacks. The algorithm is comprised of three sequential steps: correction/measurement update, GMM-based two-cluster clustering fusion and prediction/time update. Firstly, each node utilizes local measurements to update its local prior estimates of the target state and obtain the local posterior estimates of the target state. Secondly, each node conducts GMM-based two-cluster clustering in term of the dissimilarity between all local posterior estimates received from its inneighbors and its prior local estimates. The nodes with smaller dissimilarities are considered to be trusted while those with larger dissimilarities are regarded as non-trusted nodes. The estimates from the non-trusted nodes are excluded, and the trusted ones are included in the fusion step. Finally, each node utilizes the local fused estimates to predict the target state estimates (prior estimates). Experiment results demonstrate the robustness of the proposed method against five common cyberattacks, namely denial of service (DoS), random, false data injection (FDI), replay and hybrid attacks.

关键词： Industrial electronics Wireless sensor networks Target tracking Accuracy filtering Clustering algorithms filtering algorithms Prediction algorithms Robustness Computer crime

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Feedback-Based Fusion filtering for Rectangular Descriptor Systems via Amplify-and-Forward Relay Strategy

Feedback-Based Fusion Filtering for Rectangular Descriptor S...

引用

IEEE International Conference on Control Science and Systems Engineering (CCSSE)

作者： Zhaozhao Zhou Jun Hu Ruonan Luo Junhua Du Department of Applied Mathematics Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331517199

ISBN: (纸本)9798331517205

This paper is concerned with the feedback-based fusion filtering problem for linear rectangular descriptor systems. By utilizing full-order transformation, the original descriptor system is transformed into a full-order non-descriptor system. Based on the measurement output forwarded by the relay and the feedback information from the fusion center, the local filters are constructed, and the upper bound of each filtering error covariance matrix is obtained by using the basic inequality and mathematical induction. Moreover, the local filter gain is derived by calculating the minimum value of the obtained upper bound. The local information is comprehensively processed by using the covariance intersection fusion criterion and the corresponding feedback-based fusion filtering algorithm is proposed. Finally, a numerical simulation is given to verify the effectiveness of the designed filtering algorithm.

关键词： Upper bound Filters Measurement uncertainty filtering algorithms Systems engineering and theory Numerical simulation Control systems Linear matrix inequalities Covariance matrices Relays

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On the Gaussian filtering for Nonlinear Dynamic Systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods filtering Estimation filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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Distributed Dissipative filtering with Consensus on Estimation: A Two-Dimensional System Method

Distributed Dissipative Filtering with Consensus on Estimati...

引用

International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Shufen Ding Deyuan Meng Seventh Research Division Beihang University (BUAA) Beijing P. R. China School of Automation Science and Electrical Engineering Beihang University (BUAA) Beijing P. R. China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

This paper deals with the problem of distributed estimation with consensus by utilizing a two-dimensional (2-D) system approach. The consensus protocol is employed as the information fusion strategy, enabling local communication among neighboring nodes to drive their estimates toward consensus, and the distributed dissipative filter is designed on each node. It is shown that the estimation error dynamics is asymptotically stable and meets the prescribed dissipativity performance. Moreover, by incorporating consensus updates in an additional time dimension, the estimator parameters are determined through a constructed 2-D system approach, thereby reducing the design complexity. Simulations demonstrate the validity of the proposed algorithm.

关键词： Estimation error Network topology Two-dimensional displays Heuristic algorithms Process control filtering algorithms Drives Robot sensing systems Information filters Topology

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Neural Augmented Particle filtering with Learning Flock of Particles

Neural Augmented Particle Filtering with Learning Flock of P...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Itai Nuri Nir Shlezinger School of ECE Ben-Gurion University of the Negev Israel

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Particle filters (PFs) are a popular family of algorithms for state estimation in dynamic systems. The usage of PFs is often limited due to complex or approximated modelling and the need for low latency tracking. In this work, we propose to enhance PFs by augmenting their operation with a dedicated deep neural network (DNN), that learns to correct the output set of particles, which we coin flock, based on the relationships between the particles in the set itself. Our proposed neural augmentation, which can be readily incorporated into different PFs, is designed to facilitate rapid operation by maintaining accuracy with a reduced number of particles. Our method leverages data to utilize a cross-particle correction while supporting different number of particles and preserving the filter operation. We experimentally show the improvements in performance, robustness, and latency on a benchmark radar target tracking PF Algorithm, and even the mitigation of the effect of a mismatched observation modelling.

关键词： Wireless communication Training Target tracking Signal processing algorithms Artificial neural networks filtering algorithms Radar tracking Robustness State estimation Unsupervised learning

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Fast Convergence and Low Complexity Algorithm for Multichannel Adaptive filtering

Fast Convergence and Low Complexity Algorithm for Multichann...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed Zerouali Mohamed Djendi Signal Processing and Image Laboratory (LATSI) University of BLIDA 1 Blida Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

In this research, we propose a new multi-channel adaptive filtering method with low complexity and optimized convergence rate. The basic strategy behind the proposed algorithm is to generalize the decorrelation technique used in FNLMS algorithm to the multi-channel case by estimating the prediction parameters for each channel to decorrelate the multi-channel input signals. In simulation we confirm the high performances of the proposed algorithm with comparison of the two dimensions multi-channel Affine Projection (multi-channel APA) and the multi-channel Normalized Least Mean Square (multi-channel NLMS) algorithms.

关键词： Adaptive systems Echo cancellers filtering filtering algorithms Prediction algorithms Decorrelation Complexity theory Telecommunications Intelligent systems Convergence

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Collaborative filtering Algorithm in the Design of Intelligent Tourism Service Robots

Collaborative Filtering Algorithm in the Design of Intellige...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Guoquan Wang Tiantian Dong Jiayi Chen Yongchao Chu Junbing Lu Daojin Long Department of Physical Education Guangzhou Xinhua University Guangzhou Guangdong China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

As an industry that can bring huge economic effects, tourism plays a very important role in many regions. With the advancement of artificial intelligence technology, it is gradually being used in the tourism industry. One of the manifestations of smart tourism is the smart tourism service robot that is currently becoming popular. This kind of robot can provide users with more personalized and intelligent travel service recommendations. In order to better design intelligent travel service robots, this article believes that collaborative filtering algorithms can be used. In order to verify the effectiveness of this method, this article conducted a comparative experiment on the recommendation accuracy between robots based on this method and regular people. The average accuracy rate of the former was 93.50%, while the average accuracy rate of the latter was only 90.79%. The gap in experimental results fully demonstrates that the collaborative filtering algorithm can indeed design intelligent tourism service robots well.

关键词： Economics Accuracy Service robots Collaborative filtering Tourism industry filtering algorithms Data science

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Iterated Posterior Linearisation filtering for Digital Carrier Synchronisation

Iterated Posterior Linearisation Filtering for Digital Carri...

引用

International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Muyang Li Ángel F. García-Femández Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

This paper introduces a new digital carrier synchronization method that employs the Iterated Posterior Linearization Filter (IPLF) to address the challenges posed by nonlinear observation models and ionospheric scintillation in Global Navigation Satellite Systems (GNSS). Although the traditional Phase-Locked Loop (PLL) is widely used due to its simplicity and effectiveness, it exhibits limitations in high-dynamic environments such as ionospheric disturbances. This study enhances synchronization accuracy by employing Statistical Linear Regression (SLR) based on posterior probability density functions of measurement models and incorporating ionospheric scintillation phase into the state variable. Simulation results indicate that the carrier synchronization approach using IPLF achieves higher precision compared to standard PLL and non-iterative nonlinear Kalman filters.

关键词： Global navigation satellite system Accuracy Computational modeling Simulation filtering algorithms Synchronization Kalman filters Phase locked loops Standards Load modeling

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Personalized Search Engine Optimization for E-Commerce Platforms Based on Content filtering Algorithm

Personalized Search Engine Optimization for E-Commerce Platf...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Haifeng Wang School of Business Chongqing Vocational College of Transportation Chongqing China

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

With the rise of the mobile Internet era, e-commerce platforms continue to increase, and users have higher and higher requirements for service quality. In order to meet the needs of users, personalized recommendation systems have emerged as an emerging business ecosystem. This article aims to study how to build a personalized information filtering system based on content and structure, and analyze the application of e-commerce companies through actual cases. First, this article introduces content retrieval technology, search engine optimization algorithms, and keyword matching methods. Content retrieval technology is a process of information retrieval by analyzing and matching text content. Search engine optimization algorithms optimize the structure and content of web pages to rank them higher in search engines and improve the effectiveness of information retrieval. The keyword matching method is to match the keywords entered by the user with the keywords of the product or service to achieve more accurate recommendation results. This article tests the performance of the search engine. The test results show that text accounts for 51%, images account for 24%, videos account for 15%, and news accounts for 10%; the matching degree range of text is 94%-100%, the matching degree range of pictures is 80%-87%, the matching degree range of video is 76%-86%, and the matching degree range of news is 75%-82%. This meets the different information needs of users.

关键词： Accuracy Web pages Companies Search engines filtering algorithms User experience Electronic commerce Recommender systems Optimization Videos

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